New opportunity

Senior AI Engineer, Supply Chain

Sensata Technologies · Pune, India

About this role

The Senior AI Engineer, Supply Chain will design, build, and deploy enterprise AI solutions that integrate advanced AI models with business data, systems, and workflows. This role focuses on delivering scalable, production-ready applications that drive business value through modern AI technologies and engineering best practices. Responsibilities: Lead the development and deployment of enterprise AI applications from concept through production. Build AI-powered solutions including intelligent agents, copilots, and retrieval-augmented generation (RAG) applications. Develop integrations that connect AI capabilities with enterprise systems, data sources, and business processes. Build and optimize model training, evaluation, deployment, monitoring, and retraining workflows. Improve model performance, latency, cost efficiency, reliability, and explainability in production environments. Develop reusable infrastructure, tooling, libraries, and patterns that accelerate team delivery. Monitor deployed models for performance degradation, bias, drift, and operational issues. Collaborate with stakeholders to communicate technical trade-offs, risks, and recommendations. Mentor engineers and contribute to technical standards and architecture decisions. Qualifications: Required: 5+ years of software engineering experience delivering production applications. University degree (Bachelors) or equivalent experience. Strong proficiency in Python, APIs, system integration, cloud technologies, and modern software development practices. Experience developing AI, machine learning, generative AI, or intelligent automation solutions. Knowledge of software testing, CI/CD, deployment automation, and production support best practices. Experience integrating solutions with enterprise platforms, business applications, and data ecosystems. Professional proficiency in spoken and written English. Preferred: Advanced degree in Computer Science, Software Engineering, Artificial Intelligence, or a related technical field. Experience with Azure, Azure AI services, Copilot Studio, or Power Platform.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonAPIsSystem IntegrationCloud TechnologiesLarge Language Models (LLMs)Intelligent AgentsCopilotsRetrieval-Augmented Generation (RAG)CI/CDDeployment AutomationAzureAzure AI ServicesCopilot StudioPower Platform

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

End-to-End Enterprise AI Lifecycle Management
The role requires leading AI applications from initial concept through production deployment. You must demonstrate the ability to build and optimize full-stack workflows, including model training, evaluation, deployment, and continuous monitoring for performance, bias, and drift.
System Integration and Scalability
A core priority is connecting advanced AI models, such as RAG and intelligent agents, with existing enterprise systems and data ecosystems. You must focus on building reusable infrastructure and patterns that ensure these solutions are scalable, reliable, and maintainable within a complex industrial environment.
Stakeholder Collaboration and Technical Communication
You will act as a bridge between technical teams and business stakeholders. Success depends on your ability to clearly communicate technical trade-offs, risks, and project timelines while ensuring AI solutions directly drive business value in the supply chain domain.

Likely challenges to think through

Balancing Innovation with Production Stability
Integrating generative AI and RAG into established supply chain workflows presents a challenge in maintaining reliability and explainability. Prepare by discussing your approach to establishing rigorous testing, release criteria, and monitoring frameworks that prevent production issues while adopting new technologies.
Navigating Complex Enterprise Data Ecosystems
Connecting AI capabilities to legacy or disparate enterprise systems often involves significant integration hurdles. Prepare by highlighting your experience with API-driven architectures and your strategy for ensuring data security and operational compatibility when deploying AI solutions.

Practice your answers

Technical practice

Designing RAG Architectures for Enterprise

What to coverExplain your approach to building a Retrieval-Augmented Generation (RAG) pipeline. Focus on how you handle document ingestion, vector database selection, retrieval accuracy, and mitigating hallucinations when integrating with enterprise data.

Model Monitoring and Drift Detection

What to coverDescribe your strategy for monitoring AI models in production. Discuss how you detect performance degradation, data drift, and bias, and what automated workflows you implement to trigger retraining or manual intervention.

Integrating AI with Enterprise Systems

What to coverHow do you approach connecting AI models to legacy business applications and data ecosystems? Address challenges regarding API security, data latency, and ensuring reliable communication between AI services and existing enterprise workflows.

Optimizing Model Latency and Cost

What to coverWhen deploying LLMs or intelligent agents, how do you balance model performance with operational costs and latency requirements? Discuss techniques like model quantization, caching strategies, or choosing appropriate model sizes for specific use cases.

CI/CD for Machine Learning (MLOps)

What to coverDetail your experience with CI/CD pipelines specifically for AI applications. Explain how you automate testing, model validation, and deployment to ensure that updates to models do not break production integrations.

Explainability in Production AI

What to coverHow do you ensure AI model outputs are explainable to business stakeholders? Discuss tools or techniques you use to provide transparency into model decision-making processes, especially in complex supply chain scenarios.

Building Reusable AI Infrastructure

What to coverDescribe how you design reusable libraries or infrastructure patterns to accelerate team delivery. Focus on how you standardize components like data connectors, evaluation frameworks, or deployment templates.

Azure AI Services and Ecosystem

What to coverGiven the preference for Azure, explain how you would leverage Azure AI services or Copilot Studio to build enterprise-grade solutions. Discuss the trade-offs between using managed cloud services versus building custom infrastructure.

Behavioural practice

Communicating Technical Trade-offs

What to coverDescribe a time you had to explain a complex technical risk or trade-off to non-technical stakeholders. How did you ensure they understood the business impact and reached a consensus on the path forward?

Mentoring and Technical Standards

What to coverProvide an example of how you have mentored junior engineers or contributed to establishing engineering standards within a team. What was the impact on the team's overall productivity or code quality?

Managing Competing Priorities

What to coverTell me about a situation where you had to manage multiple competing demands in a fast-paced environment. How did you prioritize your tasks to ensure high-quality delivery without compromising on project timelines?

Cross-functional Collaboration

What to coverDescribe a project where you had to partner with data science, product, or data engineering teams to solve a business problem. How did you align different perspectives to deliver a successful integrated solution?

Sharpen your knowledge

Topics to prepare

Retrieval-Augmented Generation (RAG) Architecture
The role requires building RAG applications. Review the end-to-end architecture, including document ingestion, vector database selection, embedding strategies, and retrieval optimization. Exercise: Design a high-level RAG pipeline for a supply chain dataset, identifying where bottlenecks in latency or retrieval accuracy might occur.
MLOps and Production Monitoring
The position emphasizes monitoring for model drift, bias, and performance degradation. Review MLOps principles for production-ready AI. Exercise: Create a checklist for monitoring a deployed LLM, focusing on metrics for drift detection and strategies for automated retraining triggers.
Enterprise System Integration
The role involves connecting AI models with existing business data and systems. Review patterns for secure API integration and data ecosystem connectivity. Exercise: Map out how an intelligent agent would interface with a hypothetical ERP system to retrieve inventory data while maintaining security and latency standards.

Skills to revise

Python for AI Engineering
Strong Python proficiency is required for building production-ready AI. Exercise: Refactor a script that processes unstructured data into a modular, testable Python library that follows clean code principles and includes error handling for production environments.
Azure AI Services and Cloud Deployment
Experience with Azure is preferred. Exercise: Explore the documentation for Azure AI Search and Azure OpenAI Service to understand how to deploy a scalable endpoint that integrates with enterprise authentication.
CI/CD for AI Pipelines
Knowledge of deployment automation is essential. Exercise: Draft a CI/CD pipeline configuration that includes automated unit testing for code and evaluation testing for model outputs before deployment to a production environment.

Your preparation checklist

Prepare a RAG system architecture walkthrough
Develop a concise explanation of a production-grade Retrieval-Augmented Generation (RAG) application you have built. Focus on how you handled data ingestion, vector database selection, retrieval accuracy, and latency optimization to ensure the system was production-ready.
Document your MLOps and CI/CD experience
Create a summary of your experience implementing automated training, evaluation, and deployment pipelines. Highlight specific tools used for monitoring model drift, bias, and performance in production environments to demonstrate your focus on reliability.
Review Azure AI and enterprise integration capabilities
Familiarize yourself with Azure AI services, Copilot Studio, and Power Platform. Prepare to discuss how you have integrated AI models with complex enterprise systems, business applications, and data ecosystems in previous roles.
Draft examples of cross-functional stakeholder collaboration
Prepare specific examples of how you have translated complex technical AI trade-offs into business-friendly language for non-technical stakeholders, including how you managed risks and project timelines.
Formulate questions regarding supply chain AI use cases
Prepare thoughtful questions for the interviewers about the specific supply chain challenges they are looking to solve with AI, the current data maturity of their systems, and how they define success for new AI-powered agents or copilots.

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